Seldon
Deploy, monitor, and manage ML models at scale on Kubernetes
Founded
2014
Starting Price
0
Category
Developer Tools
Last updated March 12, 2026
About Seldon
Seldon is an enterprise MLOps platform that standardizes machine learning deployment with Kubernetes-native pipelines, enabling teams to serve, monitor, and explain AI models in production. It supports multi-model serving, drift detection, A/B testing, and advanced autoscaling while remaining framework-agnostic across popular ML libraries.
Pros & Cons
Pros
- Framework-agnostic with broad ML library support including scikit-learn, XGBoost, LightGBM, and MLflow
- Open-source MLServer tier allows teams to evaluate and adopt without upfront costs
- Advanced deployment strategies like A/B testing, canary, and shadow deployments reduce rollout risk
- Kubernetes-native architecture integrates naturally into existing cloud-native infrastructure
- Built-in model explainability and drift detection support regulatory compliance and trust
Key Features
Multi-Model Serving
Run multiple ML models within a single process with parallel inference and adaptive batching for optimized resource utilization and lower latency
Model Monitoring & Drift Detection
Real-time monitoring of model performance with automated drift detection and outlier identification to catch degrading predictions early
Model Explainability
Built-in explainability tools that provide transparency into model decisions, supporting compliance and trust in AI-driven outcomes
Advanced Deployment Strategies
Support for canary rollouts, A/B testing, shadow deployments, and blue-green deployments for safe, disruption-free model updates
Kubernetes-Native Pipelines
Production-ready inference pipelines built on Kubernetes with auto-scaling via HPA, enabling seamless scaling based on demand
Framework-Agnostic Serving
Supports scikit-learn, XGBoost, LightGBM, MLflow, TensorFlow, and custom runtimes via MLServer with REST and gRPC interfaces
MLServer (Open Source)
Pricing
MLServer
0/month
- Open-source inference server
- REST and gRPC endpoints
- Multi-model serving
- Adaptive batching
- Community support
Best For
Production ML Model Serving
Deploy and serve hundreds of ML models in production with low-latency inference, auto-scaling, and multi-model serving on Kubernetes clusters
Regulated Industry AI Compliance
Use built-in explainability and monitoring to meet audit and compliance requirements in finance, healthcare, and insurance
A/B Testing ML Models
Run controlled experiments comparing model versions in production with canary rollouts and traffic splitting to validate improvements safely
Real-Time Fraud Detection
Serve low-latency fraud detection models with drift monitoring to catch degradation before it impacts business outcomes
